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Article(id=1276844463016247634, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1276844393709568941, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-2561.2024.10.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1711382400000, receivedDateStr=2024-03-26, revisedDate=1714147200000, revisedDateStr=2024-04-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1782353042605, onlineDateStr=2026-06-25, pubDate=1729785600000, pubDateStr=2024-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1782353042605, onlineIssueDateStr=2026-06-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1782353042605, creator=13701087609, updateTime=1782353042605, updator=13701087609, issue=Issue{id=1276844393709568941, tenantId=1146029695717560320, journalId=1235980609244409860, year='2024', volume='45', issue='10', pageStart='1999', pageEnd='2242', issueExtLink='null', onlineDate='null', pubDate='1729785600000', pubDateStr='2024-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1782353026082, creator='13701087609', updateTime=1782355588483, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1276855141311574992, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1276844393709568941, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1276855141311574993, tenantId=1146029695717560320, journalId=1235980609244409860, issueId=1276844393709568941, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2107, endPage=2116, ext={EN=ArticleExt(id=1276844463322431828, articleId=1276844463016247634, tenantId=1146029695717560320, journalId=1235980609244409860, language=EN, title=Estimation for Nitrogen Content of Macadamia Leaves Based on Optimized Spectral Sensitive Variables, columnId=1236256434120348225, journalTitle=Chinese Journal of Tropical Crops, columnName=Plant Cultivation, Physiology & Biochemistry, runingTitle=null, highlight=null, articleAbstract=

Hyper-spectral remote sensing technology was used to explore the estimation method of nitrogen content in the leaves of macadamia to achieve a rapid diagnosis of nitrogen nutrition in macadamia trees. Lincang and Xishuangbanna were chosen as the research area to obtain the spectral reflectance and nitrogen content of the leaves of macadamia varieties O.C and HAES344. Firstly, multiple mathematical transformations were performed on the original spectral reflectance using logarithmic transformation, derivative transformation, and their combinations. Then, the correlation between nitrogen content of macadamia leaves and spectral data of different transformation forms was analyzed. Under the principle of larger determination coefficient, the wavelength corresponding to the peak characteristic point in the determination coefficient curve was selected as the nitrogen sensitive wavelength, thus the corresponding spectral variables of nitrogen sensitivity were obtained. Stepwise regression was used to further optimize the nitrogen sensitive spectral variables, and the methods of multiple linear regression (MLR), partial least squares regression (PLSR), and support vector regression (SVR) were used to construct the nitrogen content estimation models for macadamia leaves. Finally, the performance of the models was tested using validation and test sets, respectively. The results showed that the MLR, PLSR and SVR models all performed well in estimation, and the ratio of performance to standard deviate (RPD) of both the validation and test sets were above 2.0. Among them, the PLSR was the optimal estimation model, its RPD of the validation set and the test set was 2.099 and 2.110, respectively. The 19 nitrogen sensitive spectral variables selected from 6 types of transformation spectral data, including reflectance (R), logarithmic transformation of reflectance (LR), first derivative of reflectance (FDR), first derivative of logarithmic transformation of reflectance (FDLR), second derivative of reflectance (SDR), and second derivative of logarithmic transformation of reflectance (SDLR) had strong stability in nitrogen spectral response. Based on the selected 19 nitrogen sensitive spectral variables, the conventional regression modeling methods could achieve good estimation results and had strong regional universality. In this study, nitrogen sensitive spectral variables were selected from a variety of transform spectral data, which provided a new idea for the nitrogen content estimation of macadamia leaves.

, authors=null, authorsList=Guiliang CHEN, Xiaoqing LI, Muguo XU, Zhongmei LIU, Shunjun GENG, Liping YANG, authorCompany=null, correspAuthors=Liping YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1276844464660414818, articleId=1276844463016247634, tenantId=1146029695717560320, journalId=1235980609244409860, language=CN, title=基于光谱敏感变量优选的澳洲坚果叶片氮素含量估算, columnId=1236256434313286224, journalTitle=热带作物学报, columnName=作物栽培与生理生化, runingTitle=null, highlight=null, articleAbstract=

利用高光谱遥感技术探索澳洲坚果叶片氮素含量估算方法，以实现澳洲坚果氮素营养快速诊断。本研究以临沧和西双版纳为研究区，获取澳洲坚果品种O.C和HAES344叶片的光谱反射率和氮素含量，首先采用对数变换、导数变换及其组合对原始光谱反射率进行多种数学变换，然后分析澳洲坚果叶片氮素含量与不同变换形式光谱数据的相关性；在决定系数较大的原则下，选择决定系数曲线图中波峰特征点对应的波长作为氮素敏感波长，从而得到相应的氮素敏感光谱变量；运用逐步回归法对氮素敏感光谱变量进一步优化，并采用多元线性回归（MLR）、偏最小二乘回归（PLSR）和支持向量回归（SVR）3种方法构建澳洲坚果叶片氮素含量估算模型；最后，分别利用验证集和测试集对构建的澳洲坚果叶片氮素含量估算模型性能进行测试。结果显示，MLR、PLSR、SVR等3种模型估算能力均表现良好，验证集和测试集的相对分析误差（RPD）均在2.0以上；其中，PLSR模型为最优估算模型，验证集和测试集的RPD分别为2.099和2.110。从反射率（R）、对数变换（LR）、一阶导数（FDR）、对数变换的一阶导数（FDLR）、二阶导数（SDR）、对数变换的二阶导数（SDLR）等6种变换光谱数据中优选的19个氮素敏感光谱变量，对氮素光谱响应具有较强的稳定性；基于优选的19个氮素敏感光谱变量，用常规的回归建模方法均能取得良好的估算效果，且具有较强的区域普适性。本研究从多种变换光谱数据中优选氮素敏感光谱变量，为澳洲坚果叶片氮素含量估算提供新思路。

, authors=

陈桂良（1984—），男，硕士，副研究员，研究方向：植物营养与3S技术应用。

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* 杨丽萍（YANG Liping），E-mail：。
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陈桂良（1984—），男，硕士，副研究员，研究方向：植物营养与3S技术应用。

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陈桂良（1984—），男，硕士，副研究员，研究方向：植物营养与3S技术应用。

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(in Chinese), articleTitle=UAV digital image-assisted monitoring on nitrogen nutrition of winter wheat in the field, refAbstract=null), Reference(id=1276844482721087958, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, doi=null, pmid=null, pmcid=null, year=2023, volume=35, issue=8, pageStart=1904, pageEnd=1914, url=null, language=null, rfNumber=[26], rfOrder=48, authorNames=郭发旭, 冯全, 杨森, 杨婉霞, journalName=浙江农业学报, refType=null, unstructuredReference=郭发旭, 冯全, 杨森, 杨婉霞. 基于无人机高光谱的马铃薯冠层叶片全氮含量反演[J]. 浙江农业学报, 2023, 35(8): 1904-1914., articleTitle=基于无人机高光谱的马铃薯冠层叶片全氮含量反演, refAbstract=null), Reference(id=1276844484411392471, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, doi=null, pmid=null, pmcid=null, year=2023, volume=35, issue=8, pageStart=1904, pageEnd=1914, url=null, language=null, rfNumber=[26], rfOrder=49, authorNames=GUO F X, FENG Q, YANG S, YANG W X, journalName=Acta Agriculturae Zhejiangensis, refType=null, unstructuredReference=GUO F X, FENG Q, YANG S, YANG W X. Inversion of leaf nitrogen content in potato canopy based on unmanned aerial vehicle hyper-spectral images[J]. Acta Agriculturae Zhejiangensis, 2023, 35(8): 1904-1914. (in Chinese), articleTitle=Inversion of leaf nitrogen content in potato canopy based on unmanned aerial vehicle hyper-spectral images, refAbstract=null), Reference(id=1276844484478501336, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, doi=null, pmid=null, pmcid=null, year=2022, volume=39, issue=5, pageStart=882, pageEnd=891, url=null, language=null, rfNumber=[27], rfOrder=50, authorNames=栗方亮, 孔庆波, 张青, 庄木来, journalName=果树学报, refType=null, unstructuredReference=栗方亮, 孔庆波, 张青, 庄木来. 琯溪蜜柚叶片氮素含量多种高光谱估算模型对比研究[J]. 果树学报, 2022, 39(5): 882-891., articleTitle=琯溪蜜柚叶片氮素含量多种高光谱估算模型对比研究, refAbstract=null), Reference(id=1276844484545610201, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, doi=null, pmid=null, pmcid=null, year=2022, volume=39, issue=5, pageStart=882, pageEnd=891, url=null, language=null, rfNumber=[27], rfOrder=51, authorNames=LI F L, KONG Q B, ZHANG Q, ZHUANG M L, journalName=Journal of Fruit Science, refType=null, unstructuredReference=LI F L, KONG Q B, ZHANG Q, ZHUANG M L. Comparative study on several hyperspectral estimation models of nitro-gen contents in Guanxi honey pomelo leaves[J]. Journal of Fruit Science, 2022, 39(5): 882-891. (in Chinese), articleTitle=Comparative study on several hyperspectral estimation models of nitro-gen contents in Guanxi honey pomelo leaves, refAbstract=null), Reference(id=1276844484629496282, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, doi=null, pmid=null, pmcid=null, year=1996, volume=17, issue=3, pageStart=489, pageEnd=500, url=null, language=null, rfNumber=[28], rfOrder=52, authorNames=JOHNSON L F, BILLOW C R, journalName=International Journal of Remote Sensing, refType=null, unstructuredReference=JOHNSON L F, BILLOW C R. Spectrometry estimation of total nitrogen concentration in Douglas-fir foliage[J]. International Journal of Remote Sensing, 1996, 17(3): 489-500., articleTitle=Spectrometry estimation of total nitrogen concentration in Douglas-fir foliage, refAbstract=null), Reference(id=1276844484696605147, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, doi=null, pmid=null, pmcid=null, year=2003, volume=null, issue=1, pageStart=73, pageEnd=80, url=null, language=null, rfNumber=[29], rfOrder=53, authorNames=薛利红, 罗卫红, 曹卫星, 田永超, journalName=遥感学报, refType=null, unstructuredReference=薛利红, 罗卫红, 曹卫星, 田永超. 作物水分和氮素光谱诊断研究进展[J]. 遥感学报, 2003(1): 73-80., articleTitle=作物水分和氮素光谱诊断研究进展, refAbstract=null), Reference(id=1276844484759519708, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, doi=null, pmid=null, pmcid=null, year=2003, volume=null, issue=1, pageStart=73, pageEnd=80, url=null, language=null, rfNumber=[29], rfOrder=54, authorNames=XUE L H, LUO W H, CAO W X, TIAN Y C, journalName=Journal of Remote Sensing, refType=null, unstructuredReference=XUE L H, LUO W H, CAO W X, TIAN Y C. Research progress on the water and nitrogen detection using spectral reflectance[J]. Journal of Remote Sensing, 2003(1): 73-80. 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Different fertilization treatments in different growth stages of macadamia

, figureFileSmall=null, figureFileBig=null, tableContent=
处理Treatment果后肥Fertilizer after picking fruit保果肥Fertilizer for protecting fruit壮果肥Fertilizer for swelling fruit
N0P0K0N=0，P=0，K=0N=0，P=0，K=0N=0，P=0，K=0
N0P1K1N=0，P=600，K=374N=0，P=300，K=280N=0，P=300，K=280
N1P1K1N=308，P=600，K=374N=231，P=300，K=280N=231，P=300，K=280
N2P1K1N=616，P=600，K=374N=462，P=300，K=280N=462，P=300，K=280
N2P0K1N=616，P=0，K=374N=462，P=0，K=280N=462，P=0，K=280
N2P1K0N=616，P=600，K=0N=462，P=300，K=0N=462，P=300，K=0
N3P1K1N=924，P=600，K=374N=693，P=300，K=280N=693，P=300，K=280
), ArticleFig(id=1276844476291219865, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=CN, label=表1, caption=

澳洲坚果在各生育期的不同施肥处理

, figureFileSmall=null, figureFileBig=null, tableContent=
处理Treatment果后肥Fertilizer after picking fruit保果肥Fertilizer for protecting fruit壮果肥Fertilizer for swelling fruit
N0P0K0N=0，P=0，K=0N=0，P=0，K=0N=0，P=0，K=0
N0P1K1N=0，P=600，K=374N=0，P=300，K=280N=0，P=300，K=280
N1P1K1N=308，P=600，K=374N=231，P=300，K=280N=231，P=300，K=280
N2P1K1N=616，P=600，K=374N=462，P=300，K=280N=462，P=300，K=280
N2P0K1N=616，P=0，K=374N=462，P=0，K=280N=462，P=0，K=280
N2P1K0N=616，P=600，K=0N=462，P=300，K=0N=462，P=300，K=0
N3P1K1N=924，P=600，K=374N=693，P=300，K=280N=693，P=300，K=280
), ArticleFig(id=1276844476362523034, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=EN, label=Tab. 2, caption=

General situation of macadamia leaf samples

, figureFileSmall=null, figureFileBig=null, tableContent=
采样日期Sampling date采样果园Sampling orchard研究区Research area叶片品种（数量）Leaf variety（number）定植年份Planting year
2020-03-16云南省热带作物科学研究所景哈坚果基地西双版纳O.C（40）1998
2020-03-17云南绿野农林集团有限公司南糯山坚果基地西双版纳O.C（28）2012
2020-03-18景洪市勐养镇勐养农场西双版纳O.C（27）2006
2020-03-19云南省热带作物科学研究所六队坚果试验基地西双版纳O.C（24）2012
2021-01-19临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（21）；HAES344（21）1998
2021-02-20云南绿野农林集团有限公司南糯山坚果基地西双版纳O.C（14）；HAES344（14）2012
2021-05-13临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（21）；HAES344（21）1998
2021-09-10临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（21）；HAES344（21）1998
2022-04-26临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（21）；HAES344（21）1998
2022-07-13临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（14）；HAES344（14）1998
2022-09-14临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（14）；HAES344（14）1998
), ArticleFig(id=1276844476425437595, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=CN, label=表2, caption=

澳洲坚果叶片样品概况

, figureFileSmall=null, figureFileBig=null, tableContent=
采样日期Sampling date采样果园Sampling orchard研究区Research area叶片品种（数量）Leaf variety（number）定植年份Planting year
2020-03-16云南省热带作物科学研究所景哈坚果基地西双版纳O.C（40）1998
2020-03-17云南绿野农林集团有限公司南糯山坚果基地西双版纳O.C（28）2012
2020-03-18景洪市勐养镇勐养农场西双版纳O.C（27）2006
2020-03-19云南省热带作物科学研究所六队坚果试验基地西双版纳O.C（24）2012
2021-01-19临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（21）；HAES344（21）1998
2021-02-20云南绿野农林集团有限公司南糯山坚果基地西双版纳O.C（14）；HAES344（14）2012
2021-05-13临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（21）；HAES344（21）1998
2021-09-10临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（21）；HAES344（21）1998
2022-04-26临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（21）；HAES344（21）1998
2022-07-13临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（14）；HAES344（14）1998
2022-09-14临沧市双江县勐勐镇小黑江酒厂坚果基地临沧O.C（14）；HAES344（14）1998
), ArticleFig(id=1276844476509323676, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=EN, label=Tab. 3, caption=

Descriptive statistics of nitrogen content in macadamia leaf samples for model calibration, validation, and testing

, figureFileSmall=null, figureFileBig=null, tableContent=
样品集类型Sample set type样本数Sample size最小值Minimum最大值Maximum平均值Mean标准差Standard deviation叶片品种（数量）Leaf variety（number）
校正集19710.2522.4515.562.30HAES344（97）；O.C（100）
验证集4910.4821.5015.342.06HAES344（24）；O.C（25）
测试集11911.1821.4616.502.15O.C（119）
), ArticleFig(id=1276844476572238237, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=CN, label=表3, caption=

用于模型校正、验证和测试的澳洲坚果叶片样品氮素含量描述性统计

, figureFileSmall=null, figureFileBig=null, tableContent=
样品集类型Sample set type样本数Sample size最小值Minimum最大值Maximum平均值Mean标准差Standard deviation叶片品种（数量）Leaf variety（number）
校正集19710.2522.4515.562.30HAES344（97）；O.C（100）
验证集4910.4821.5015.342.06HAES344（24）；O.C（25）
测试集11911.1821.4616.502.15O.C（119）
), ArticleFig(id=1276844476651930014, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=EN, label=Tab. 4, caption=

Stepwise regression and collinear diagnosis results

, figureFileSmall=null, figureFileBig=null, tableContent=
回归项Regression itemsTP容差Tolerance方差膨胀因子Variance Inflation Factor
常量7.0683.479E-11
LR474-2.9530.0040.2603.842
FDR1072-4.4331.626E-050.5851.709
FDR1414-1.7570.0810.1427.020
FDR16316.3331.926E-090.3283.049
FDR18373.0760.0020.1636.136
FDR20637.3726.222E-120.2444.091
FDR2154-12.9721.779E-270.1377.275
FDLR492-2.4020.0170.2753.636
FDLR16723.5180.0010.3822.615
FDLR2227-4.4731.377E-050.1735.784
FDLR23893.1430.0020.2504.008
FDLR24283.4580.0010.2863.496
SDR587-3.4290.0010.2084.798
SDR1595-2.2010.0290.4842.068
SDR17693.6643.277E-040.7511.331
SDR1780-2.0500.0420.3592.785
SDR19482.9580.0040.2933.412
SDLR2153-4.2803.058E-050.2853.509
SDLR23282.8760.0050.8011.249
), ArticleFig(id=1276844476735816095, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=CN, label=表4, caption=

逐步回归和共线性诊断结果

, figureFileSmall=null, figureFileBig=null, tableContent=
回归项Regression itemsTP容差Tolerance方差膨胀因子Variance Inflation Factor
常量7.0683.479E-11
LR474-2.9530.0040.2603.842
FDR1072-4.4331.626E-050.5851.709
FDR1414-1.7570.0810.1427.020
FDR16316.3331.926E-090.3283.049
FDR18373.0760.0020.1636.136
FDR20637.3726.222E-120.2444.091
FDR2154-12.9721.779E-270.1377.275
FDLR492-2.4020.0170.2753.636
FDLR16723.5180.0010.3822.615
FDLR2227-4.4731.377E-050.1735.784
FDLR23893.1430.0020.2504.008
FDLR24283.4580.0010.2863.496
SDR587-3.4290.0010.2084.798
SDR1595-2.2010.0290.4842.068
SDR17693.6643.277E-040.7511.331
SDR1780-2.0500.0420.3592.785
SDR19482.9580.0040.2933.412
SDLR2153-4.2803.058E-050.2853.509
SDLR23282.8760.0050.8011.249
), ArticleFig(id=1276844476807119264, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=EN, label=Tab. 5, caption=

Estimation performance of nitrogen content estimation models for macadamia leaves

, figureFileSmall=null, figureFileBig=null, tableContent=
建模方法Modeling method建模集（n=197）Calibration set（n=197）验证集（n=49）Validation set（n=49）测试集（n=119）Test set（n=119）
R2RMSERPDR2RMSERPDR2RMSERPD
MLR0.8580.8522.7020.8001.0232.0100.7961.0302.087
PLSR0.8410.9172.5110.8130.9792.0990.8061.0182.110
SVR0.8520.8852.6020.8090.9482.1680.8141.0522.042
), ArticleFig(id=1276844476882616737, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=CN, label=表5, caption=

澳洲坚果叶片氮素含量估算模型的估算性能

, figureFileSmall=null, figureFileBig=null, tableContent=
建模方法Modeling method建模集（n=197）Calibration set（n=197）验证集（n=49）Validation set（n=49）测试集（n=119）Test set（n=119）
R2RMSERPDR2RMSERPDR2RMSERPD
MLR0.8580.8522.7020.8001.0232.0100.7961.0302.087
PLSR0.8410.9172.5110.8130.9792.0990.8061.0182.110
SVR0.8520.8852.6020.8090.9482.1680.8141.0522.042
), ArticleFig(id=1276844476958114210, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=EN, label=Tab. 6, caption=

Comparison between the optimal model constructed in this study and the full band PLSR models

, figureFileSmall=null, figureFileBig=null, tableContent=
建模方法Modeling method光谱变换Spectral transformation变量个数Variable number建模集（n=197）Calibration set（n=197）验证集（n=49）Validation set（n=49）测试集（n=119）Test set（n=119）
R2RMSERPDR2RMSERPDR2RMSERPD
PLSRLR/FDR/FDLR/SDR/SDLR190.8410.9172.5110.8130.9792.0990.8061.0182.110
PLSRR21010.8530.8672.6550.8830.7272.8260.7212.1720.989
PLSRLR21010.8330.9252.4870.8150.8842.3250.7232.0551.046
PLSRFDR20990.7581.1292.0390.7291.1511.7850.6241.8911.136
PLSRFDLR20990.7491.1512.0000.6781.2461.6500.5022.4550.875
PLSRSDR20970.6441.3701.6810.4111.6841.2210.1042.2040.975
PLSRSDLR20970.3431.8601.2370.3831.6191.2700.3262.1620.994
), ArticleFig(id=1276844477062971811, tenantId=1146029695717560320, journalId=1235980609244409860, articleId=1276844463016247634, language=CN, label=表6, caption=

本研究构建的最优模型与全波段PLSR模型比较

, figureFileSmall=null, figureFileBig=null, tableContent=
建模方法Modeling method光谱变换Spectral transformation变量个数Variable number建模集（n=197）Calibration set（n=197）验证集（n=49）Validation set（n=49）测试集（n=119）Test set（n=119）
R2RMSERPDR2RMSERPDR2RMSERPD
PLSRLR/FDR/FDLR/SDR/SDLR190.8410.9172.5110.8130.9792.0990.8061.0182.110
PLSRR21010.8530.8672.6550.8830.7272.8260.7212.1720.989
PLSRLR21010.8330.9252.4870.8150.8842.3250.7232.0551.046
PLSRFDR20990.7581.1292.0390.7291.1511.7850.6241.8911.136
PLSRFDLR20990.7491.1512.0000.6781.2461.6500.5022.4550.875
PLSRSDR20970.6441.3701.6810.4111.6841.2210.1042.2040.975
PLSRSDLR20970.3431.8601.2370.3831.6191.2700.3262.1620.994
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基于光谱敏感变量优选的澳洲坚果叶片氮素含量估算
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陈桂良 , 黎小清 , 许木果 , 刘忠妹 , 耿顺军 , 杨丽萍 *
热带作物学报 | 作物栽培与生理生化 2024,45(10): 2107-2116
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热带作物学报 |作物栽培与生理生化 2024 , 45 (10) : 2107 -2116
基于光谱敏感变量优选的澳洲坚果叶片氮素含量估算
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陈桂良, 黎小清, 许木果, 刘忠妹, 耿顺军, 杨丽萍*
作者信息
  • 云南省热带作物科学研究所，云南景洪　666100
通讯作者:
* 杨丽萍（YANG Liping），E-mail：。
Estimation for Nitrogen Content of Macadamia Leaves Based on Optimized Spectral Sensitive Variables
Guiliang CHEN, Xiaoqing LI, Muguo XU, Zhongmei LIU, Shunjun GENG, Liping YANG*
Affiliations
  • Yunnan Institute of Tropical Crops, Jinghong, Yunnan 666100, China
出版时间: 2024-10-25 doi: 10.3969/j.issn.1000-2561.2024.10.012
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摘要
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利用高光谱遥感技术探索澳洲坚果叶片氮素含量估算方法，以实现澳洲坚果氮素营养快速诊断。本研究以临沧和西双版纳为研究区，获取澳洲坚果品种O.C和HAES344叶片的光谱反射率和氮素含量，首先采用对数变换、导数变换及其组合对原始光谱反射率进行多种数学变换，然后分析澳洲坚果叶片氮素含量与不同变换形式光谱数据的相关性；在决定系数较大的原则下，选择决定系数曲线图中波峰特征点对应的波长作为氮素敏感波长，从而得到相应的氮素敏感光谱变量；运用逐步回归法对氮素敏感光谱变量进一步优化，并采用多元线性回归（MLR）、偏最小二乘回归（PLSR）和支持向量回归（SVR）3种方法构建澳洲坚果叶片氮素含量估算模型；最后，分别利用验证集和测试集对构建的澳洲坚果叶片氮素含量估算模型性能进行测试。结果显示，MLR、PLSR、SVR等3种模型估算能力均表现良好，验证集和测试集的相对分析误差（RPD）均在2.0以上；其中，PLSR模型为最优估算模型，验证集和测试集的RPD分别为2.099和2.110。从反射率（R）、对数变换（LR）、一阶导数（FDR）、对数变换的一阶导数（FDLR）、二阶导数（SDR）、对数变换的二阶导数（SDLR）等6种变换光谱数据中优选的19个氮素敏感光谱变量，对氮素光谱响应具有较强的稳定性；基于优选的19个氮素敏感光谱变量，用常规的回归建模方法均能取得良好的估算效果，且具有较强的区域普适性。本研究从多种变换光谱数据中优选氮素敏感光谱变量，为澳洲坚果叶片氮素含量估算提供新思路。

澳洲坚果  /  高光谱  /  氮素营养  /  光谱变量  /  估算模型

Hyper-spectral remote sensing technology was used to explore the estimation method of nitrogen content in the leaves of macadamia to achieve a rapid diagnosis of nitrogen nutrition in macadamia trees. Lincang and Xishuangbanna were chosen as the research area to obtain the spectral reflectance and nitrogen content of the leaves of macadamia varieties O.C and HAES344. Firstly, multiple mathematical transformations were performed on the original spectral reflectance using logarithmic transformation, derivative transformation, and their combinations. Then, the correlation between nitrogen content of macadamia leaves and spectral data of different transformation forms was analyzed. Under the principle of larger determination coefficient, the wavelength corresponding to the peak characteristic point in the determination coefficient curve was selected as the nitrogen sensitive wavelength, thus the corresponding spectral variables of nitrogen sensitivity were obtained. Stepwise regression was used to further optimize the nitrogen sensitive spectral variables, and the methods of multiple linear regression (MLR), partial least squares regression (PLSR), and support vector regression (SVR) were used to construct the nitrogen content estimation models for macadamia leaves. Finally, the performance of the models was tested using validation and test sets, respectively. The results showed that the MLR, PLSR and SVR models all performed well in estimation, and the ratio of performance to standard deviate (RPD) of both the validation and test sets were above 2.0. Among them, the PLSR was the optimal estimation model, its RPD of the validation set and the test set was 2.099 and 2.110, respectively. The 19 nitrogen sensitive spectral variables selected from 6 types of transformation spectral data, including reflectance (R), logarithmic transformation of reflectance (LR), first derivative of reflectance (FDR), first derivative of logarithmic transformation of reflectance (FDLR), second derivative of reflectance (SDR), and second derivative of logarithmic transformation of reflectance (SDLR) had strong stability in nitrogen spectral response. Based on the selected 19 nitrogen sensitive spectral variables, the conventional regression modeling methods could achieve good estimation results and had strong regional universality. In this study, nitrogen sensitive spectral variables were selected from a variety of transform spectral data, which provided a new idea for the nitrogen content estimation of macadamia leaves.

macadamia  /  hyper-spectral  /  nitrogen nutrition  /  spectral variable  /  estimation model
陈桂良, 黎小清, 许木果, 刘忠妹, 耿顺军, 杨丽萍. 基于光谱敏感变量优选的澳洲坚果叶片氮素含量估算. 热带作物学报, 2024 , 45 (10) : 2107 -2116 . DOI: 10.3969/j.issn.1000-2561.2024.10.012
Guiliang CHEN, Xiaoqing LI, Muguo XU, Zhongmei LIU, Shunjun GENG, Liping YANG. Estimation for Nitrogen Content of Macadamia Leaves Based on Optimized Spectral Sensitive Variables[J]. Chinese Journal of Tropical Crops, 2024 , 45 (10) : 2107 -2116 . DOI: 10.3969/j.issn.1000-2561.2024.10.012
正文
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澳洲坚果（Macadamia spp.）是山龙眼科（Proteaceae）澳洲坚果属（Macadamia F. Muell）常绿乔木果树，原产于澳大利亚，有“坚果之王”的美誉[1]。截至2020年底，全国澳洲坚果的种植面积为26.61万hm2，居世界第一位，其中云南达23.53万hm2[2]，已成为云南边疆地区农民增收、企业增效、财政增长的新兴产业。澳洲坚果是多年生经济作物，其营养状况直接关系到产量、品质以及树体生产年限等。由于叶片养分含量直接反映树体的营养状况[3-4]，可以根据澳洲坚果叶片中营养元素的丰缺程度进行针对性施肥，为果树及时补给养分[5]。当前，澳洲坚果叶片营养诊断施肥大多基于传统的叶片化学分析，虽然具有较高的检测精度，但存在消耗时间长、过程繁琐、工作量大、时效性差等弊端。然而，植物在缺乏营养元素时，会引起叶片颜色、厚度、水分含量以及形态结构等一系列变化，从而引起光谱反射率特征的变化。高光谱遥感技术因其光谱分辨率高，能够探测到地物在光谱特征上的微小差异，故而广泛用于作物营养快速检测[6]。因此，基于高光谱遥感数据和营养元素分析，探索利用高光谱遥感技术估测澳洲坚果叶片营养元素含量，对监测澳洲坚果树的生长势、促进澳洲坚果园的精细化管理具有重要的理论与现实意义。
氮素是合成蛋白质和叶绿素的重要组成部分，并参与酶的合成，其含量直接影响果树光合作用强度与糖类物质的形成，缺氮或者过量施氮都将造成产量和果实品质的下降。目前，将高光谱信息应用到作物氮素营养快速诊断的研究大多聚焦在小麦、水稻等短期作物。FERNANDEZ等[7]发现660 nm（红光）和545 nm（绿光）的线性组合可以作为光谱变量预测小麦叶片的氮素含量，李粉玲等[8]则利用550~770 nm波段的吸收峰总面积来定量估算冬小麦叶片氮素含量水平，薛利红等[9]发现水稻冠层近红外与绿光波段的比值（R810/R560）与叶片氮积累量呈显著线性关系，白丽敏等[10]采用连续投影算法、结合偏最小二乘法建立了冬小麦拔节期叶片氮含量高光谱估算模型，张玉森等[11]基于偏最小二乘法建立了水稻新鲜叶片和干叶粉末的光谱氮素预测模型。另外，部分学者也将高光谱营养诊断技术向多年生经济作物扩展。朱晓铃等[12]发现对数变换光谱的983、1245、1316、1457 nm等4个波段组合可用于估测蜜柚叶片氮浓度，李金梦等[13]采用连续投影算法、结合反向传播人工神经网络模型建立了柑橘树叶片含氮量预测模型，黎小清等[14]分别采用光谱指数法和偏最小二乘法构建了橡胶树叶片氮素含量高光谱估算模型，林灵辰等[15]发现基于6个叶片光谱参数所构建的支持向量回归模型能够较为准确地估测毛竹叶片氮元素含量，马文强等[16]采用组合预处理方法并结合连续投影算法构建了核桃叶片氮元素含量的PLSR预测模型。在澳洲坚果上，DE SILVA等[17]基于400~1000 nm全波段高光谱反射率数据建立了叶片氮素含量估算模型，但测试集R2仅为0.55。然而，氮素光谱数据的获得受到作物种类、种植模式、区域条件等因素的影响，进而导致基于高光谱遥感技术的氮素诊断施肥方法和模型尚需进一步探索。
研究表明，通过敏感波段、植被指数、高光谱特征参数的筛选，消除光谱波段的冗余信息后再建立预测模型，预测精度明显提升[16,18]。此外，不同光谱变换也会影响模型的预测精度，通过对原始光谱进行光谱变换，可增强光谱响应，降低干扰影响，有效提高模型精度[19]。在现有的敏感变量提取研究中，大多是从多种数学变换处理中寻找一种最优的处理方法，然后从最优变换数据中筛选特征波长[20-21]。本研究拟以澳洲坚果叶片为研究对象，分析叶片氮素含量和光谱特征，充分利用可用的光谱信息，尝试从多种变换光谱数据中优选氮素敏感光谱变量，并基于优选的氮素敏感光谱变量建立澳洲坚果叶片氮素含量估算模型，可为今后进行澳洲坚果叶片氮素营养监测指导施用氮肥提供技术支撑。
1　材料与方法
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1.1　材料
1.1.1　田间试验
于2020年在临沧市双江县勐勐镇小黑江酒厂坚果基地布置澳洲坚果施肥试验。试验地地理位置23°24′5″N，99°44′22″E，海拔977 m，该基地0~20 cm的土壤理化性质如下：pH 5.28，有机质为35.66 g/kg，全氮为1.43 g/kg，水解氮为119.95 mg/kg，有效磷为27.85 mg/kg，速效钾为183.09 mg/kg。用于试验的澳洲坚果品种为O.C和HAES344，1998年种植，株行距4.0 m×6.0 m；各品种分别选择长势较一致的35株树用于试验。试验设7个施肥处理，每个处理5株，N为尿素，P为钙镁磷，K为硫酸钾，于每年分3次施肥：3月（保果肥）、6月（壮果肥）、10月（果后肥），各个时期的施肥量数据见表1。
1.1.2　叶片样品采集
叶片采集在晴天的上午9：00—11：00进行，采集无病虫害的成熟叶片。对于选取的澳洲坚果采样树，在树冠的同一高度、同一方向采集颜色和形态特征基本一致的5片叶作为1个样品。在临沧市双江县勐勐镇小黑江酒厂坚果基地试验区，考虑到澳洲坚果叶片氮素含量的季节性变化，选择多个月份进行样品采集，共采集6次；每次从各处理的5株澳洲坚果树中随机选择2或3株进行采样，累计采集224个叶片样品。为了尽可能获得不同氮素营养水平的叶片样本，本研究还在西双版纳州4个澳洲坚果园以同样的方法进行了5次随机采样，共采集147个叶片样品。因此，用于本研究的叶片样品为371个（表2）。
1.2　方法
1.2.1　叶片光谱与氮素含量测定
鲜叶采集后迅速装入自封袋，储存于移动冷藏箱中，12 h内完成鲜叶的光谱反射率测定。采用FieldSpec4光谱仪（美国ASD公司产）测定叶片光谱反射率，测定光源由植物探头提供，测定叶片正面的光谱反射率。测量前先进行参考白板校正，测量时利用叶片夹持器将叶片固定，选取叶片中部无病斑区域，测定叶片正面的光谱反射率，连续扫描3次，每个叶片样品的光谱反射率由15条光谱曲线取平均而得。去除噪声较大的350~399 nm波长，仅保留400~2500 nm光谱反射率用于本研究。将已采集光谱反射率的澳洲坚果叶片，烘干后粉碎过40目筛，采用连续流动分析法测定叶片氮含量[22]。
1.2.2　样本数据集划分
2021—2022年采集的252个样品中，先剔除2个光谱曲线明显异常的样品后，再采用“平均值±3倍标准差”的方法剔除4个氮素含量异常样品，以剩余的246个叶片样品用于模型校正和验证，其中O.C品种叶片样品125个，HAES344品种叶片样品121个。采用分层随机抽样方法划分校正集和验证集，结合品种、采样点和采样日期，将246个样品分为14个类型，每个类型分别抽取80%的样品作为校正集，剩余的样品作为验证集，将抽取的各类型样品按校正集和验证集进行合并。将2020年采集的119个样品作为测试集。表3为用于模型校正、验证和测试的澳洲坚果叶片样品氮素含量描述性统计。
1.2.3　对数变换
本研究的对数变换是对光谱反射率R的倒数求对数log（1/R）。光谱反射率R经对数变换后，可以增强原始光谱反射率值较低的波段（如可见光波段）的光谱差异。
1.2.4　导数变换
导数变换是最常用的高光谱变换形式之一，通过导数变换可以减弱或消除背景、噪声光谱对目标光谱的影响。一阶导数光谱（FD）、二阶导数光谱（SD）的近似计算方法如下：
式中，λi是波段i的波长值，R（λi）是波长λi的反射率，Δλ是波长λi-1到波长λi的差值。
1.2.5　氮素敏感光谱变量提取
由于决定系数大小反映的是光谱数据与叶片氮素含量的相关性高低，因此决定系数可以用于变量筛选。通过分析澳洲坚果叶片氮素含量与不同变换形式光谱数据的相关性，在决定系数较大的原则下，选择决定系数曲线图中波峰特征点对应的波长作为氮素敏感波长。在本研究中，当决定系数同时满足以下5个条件时，定义DC（λi）为波峰特征点：
式中，λi是波段i的波长值，DC（λi）是波长λi对应光谱与叶片氮素含量的决定系数。
本研究用于模型构建的校正集样本数为197个，当决定系数DC（λi）>0.04时，已经处于0.01水平上显著相关。通过上述方法提取得到不同变换形式光谱数据的氮素敏感波长，从而得到相应的氮素敏感光谱变量。
本研究选取决定系数曲线图中波峰特征点作为氮素敏感波长，主要依据有：（1）波峰特征点对应的决定系数是某个波段范围内的最大值；（2）这些特征点的两侧波长变量通常与特征点波长变量有较强的共线性，因此去除特征点以外的波长。
1.2.6　模型建立与评价
选择多元线性回归（MLR）、偏最小二乘回归（PLSR）、支持向量回归（SVR）等3种建模方法[23]，建立澳洲坚果叶片氮素含量估算模型。采用决定系数（R2）、均方根误差（RMSE）和相对分析误差（RPD）进行模型评价，R2越接近1，RMSE越小，RPD越大，说明模型估算效果越好。当RPD在1.4~2时表示模型有一定的估算能力，在2~2.5时表示模型估算能力良好，大于2.5时则表明模型有很好的估算能力[24]。
2　结果与分析
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2.1　澳洲坚果叶片氮素含量与多种变换光谱数据的相关性分析
将197个校正集样品用于分析澳洲坚果叶片光谱反射率及其变换光谱与叶片氮含量的相关性。对原始光谱反射率R先进行log（1/R）计算，得到LR，再对R和LR进行1阶导数变换得到FDR和FDLR，最后对R和LR进行2阶导数变换得到SDR和SDLR，建立澳洲坚果叶片氮素含量与光谱反射率R及其变换形式光谱的相关系数曲线图。结果如图1所示，R、LR、FDR、FDLR、SDR、SDLR的最高相关系数分别为-0.507、0.515、-0.540、-0.584、0.618、-0.631，分别出现在528、527、2154、2070、532、522 nm处。相比原始光谱反射率R，经对数变换或导数变换处理后，光谱数据与叶片氮素含量的相关性得到了增强。从图1来看，导数变换后，虽然也会一定程度放大光谱噪声，但大大增强了敏感波段光谱对氮素的响应能力。
2.2　澳洲坚果叶片氮素敏感光谱变量优选
对图1的相关系数进行平方运算，得到澳洲坚果叶片氮素含量与光谱反射率及其变换形式数据的决定系数曲线图（图2）。依据氮素敏感光谱变量提取方法，在决定系数大于0.04的原则下，选择决定系数曲线图中波峰特征点对应的波长作为氮素敏感波长。在氮素敏感波长提取过程中，去除决定系数较低的波长的同时，还剔除了与波峰特征点波长变量存在较强共线性的波峰特征点两侧波长变量，尽可能保留了可用的光谱信息。通过氮素敏感波长初步筛选，6种形式光谱数据的变量总数从12 594个压缩到619个，压缩率达95%。其中，R、LR、FDR、FDLR、SDR、SDLR提取得到的氮素敏感波长个数分别为33、37、138、145、139、127。
从图3可以看出，R与LR，FDR与FDLR，以及SDR与SDLR的敏感波长分布基本一致，这是因为R与LR，FDR与FDLR，以及SDR与SDLR在相同波长处呈现高相关性。虽然在上述氮素敏感波长提取过程中，已经消除了各变换形式光谱内部的部分冗余波长，但不同变换形式光谱之间还存在较大的共线性问题。
为消除共线性的影响，采用逐步回归法对619个氮素敏感光谱变量进一步优化。以619个氮素敏感光谱变量为自变量，叶片氮素含量为因变量，将619个氮素敏感光谱变量与叶片氮素含量进行逐步回归分析。逐步回归和共线性诊断结果如表4所示，对模型的贡献达到显著水平而被留在模型中的变量有19个。通过模型中变量共性线诊断发现，变量中方差膨胀因子均小于10，说明变量间不存在多重共线性[25]。最终确认不存在共线性的19个优选氮素敏感光谱变量分别是：LR474、FDR1072、FDR1414、FDR1631、FDR1837、FDR2063、FDR2154、FDLR492、FDLR1672、FDLR2227、FDLR2389、FDLR2428、SDR587、SDR1595、SDR1769、SDR1780、SDR1948、SDLR2153、SDLR2328。其中，变量入选最多的光谱类型是FDR、FDLR和SDR，氮素敏感波长主要分布在近红外波段1072~2428 nm（图4）。
2.3　澳洲坚果叶片氮素含量估算模型的建立与验证
将19个优选的澳洲坚果叶片氮素敏感光谱变量作为自变量，叶片氮素含量作为因变量，并采用MLR、PLSR、SVR等3种方法构建澳洲坚果叶片氮素含量高光谱估算模型。分别利用验证集和测试集对构建的澳洲坚果叶片氮素含量高光谱估算模型性能进行测试，结果如表5所示，MLR、PLSR、SVR等3种模型估算能力均表现良好，验证集和测试集的RPD均在2.0以上。综合来看，PLSR模型为最优估算模型，验证集和测试集的RPD分别为2.099与2.110，估算能力良好。由于测试集样品全部采集于西双版纳州，而建模集和验证集约88.6%的样品采集于临沧市，从测试集的估算效果来看，本研究构建的模型具有较强的区域普适性。
为了进一步验证本研究构建模型的普适性，将构建的光谱最优模型与常规的全波段PLSR模型进行性能和普适性对比（表6）。从验证集的估算表现来看，基于全波段原始光谱反射率（R）构建的PLSR模型效果最好。但从测试集的估算结果来看，本研究构建的模型估算性能大大优于6种变换光谱的全波段PLSR模型，其RPD为2.110，而全波段PLSR模型的RPD最高仅为1.136。结果表明，该光谱最优模型的区域普适性优于全波段PLSR模型，适合推广应用。
3　讨论
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本研究首先采用对数变换、导数变换及变换组合对原始光谱反射率进行多种数学变换，然后分析了澳洲坚果叶片氮素含量与多种变换光谱数据的相关性。结果发现，R、LR、FDR、FDLR、SDR、SDLR与澳洲坚果叶片氮素含量的最高相关系数分别为-0.507、0.515、-0.540、-0.584、0.618与-0.631，分别出现在528、527、2154、2070、532、522 nm处。这表明，经对数变换、导数变换及变换组合处理后，各光谱数据与氮素含量的相关性均得到提升。其中，单一变换处理以二阶导数变换的提升效果最明显，与郭发旭等[26]的研究结果一致；组合变换处理则以对数变换结合二阶导数变换的提升效果最明显。本研究中，叶片氮素含量与R的最高相关性在528 nm波段，与前人在橡胶[14]和琯溪蜜柚[27]上的研究结果相似，但也有一定差异，这可能是作物类型的差异所致。JOHNSON等[28]研究发现道格拉斯冷杉叶片氮素含量与FDR在2160 nm波段处的相关性最高，与本研究发现澳洲坚果叶片氮素含量与FDR最高相关性在2154 nm波段处的结果基本一致。
大量研究表明，叶片氮素敏感波段主要在可见光波段[7-8]。本研究通过分析决定系数，筛选到619个氮素敏感光谱变量，并采用逐步回归法进一步优选到不存在共线性影响的19个氮素敏感光谱变量，其波长主要分布在近红外波段的1072~2428 nm区间，这与朱晓铃等[12]的研究相似。薛利红等[29]认为，叶片中的氮素大多以蛋白质的形态存在，而2100 nm左右为蛋白质的吸收波段，因此2100 nm左右也是氮素的敏感波段，这在本研究中得到证实。本研究优选的19个氮素敏感光谱变量中，最多的光谱类型是FDR、FDLR和SDR，入选变量个数分别为6、5和5，这进一步表明对数变换、导数变换及变换组合处理能够有效增强敏感波段光谱对氮素的响应能力。本研究发现，与澳洲坚果叶片氮素含量最高相关的6个光谱变量中仅有FDR2154入选为最优的氮素敏感光谱变量，这是逐步回归法的变量优选结果，但不同的变量优选方法可能会呈现不一样的结果。
为得到最优的澳洲坚果叶片氮素含量高光谱估算模型，比较了MLR、PLSR与SVR等3种模型的估算效果。结果显示，PLSR模型的估算效果要优于MLR和SVR，其验证集和测试集的RPD分别为2.099及2.110，表明PLSR模型具有较高的估算精度。与6种变换光谱的全波段PLSR模型相比，经优选氮素敏感光谱变量后的PLSR模型效果最好，表明其具有较强的区域普适性，可能更适用于澳洲坚果叶片氮素含量的实时预测，为澳洲坚果树科学合理施加氮肥提供了一定的参考依据。同时，基于多种变换光谱数据，优选氮素敏感光谱变量，充分利用了不同变换光谱数据中可用的光谱信息，为光谱敏感变量的筛选提供新思路。
基金
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  • 云南省基础研究专项面上项目(202101AT070146)
  • 云南省热带作物科技创新体系建设专项(RF2024-11)
文献
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2024年第45卷第10期
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doi: 10.3969/j.issn.1000-2561.2024.10.012
  • 接收时间：2024-03-26
  • 首发时间：2026-06-25
  • 出版时间：2024-10-25
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  • 收稿日期：2024-03-26
  • 修回日期：2024-04-27
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云南省基础研究专项面上项目(202101AT070146)
云南省热带作物科技创新体系建设专项(RF2024-11)
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    云南省热带作物科学研究所，云南景洪　666100

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* 杨丽萍（YANG Liping），E-mail：。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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